Stories

Summer 2017 the idea for Project Aiur was born, stemming from a frustration of being forced into a corporate B2B sales-corner by the financial bottom line-minded Venture Capitalists we were pitching to. Being an impact focused company, we realized that selling B2B SaaS to big corporates, while a great business model, would not move the needle for the frustrated individual researcher, and would not bring about the necessary change in a broken industry.

Now the Project Aiur whitepaper is out, and it has out of necessity taken a quite different path than the Iris.ai core commercial business. Here is how the two; Iris.ai, the commercial entity and Project Aiur, the not-for-profit to Iris.ai, community-owned initiative are related to each other.

1. Company structure

Project Aiur is what it says: a project. It has been ideated by Iris.ai to create an open, community-governed AI Engine for Knowledge Validation.

Project Aiur is initiated and jurisdictionally operated from Iris.ai BG EOOD, the Bulgarian subsidiary of the Iris.ai group. We have our main development office in Sofia, Bulgaria, which is both a member part of the European Union and one of the more favorable crypto jurisdictions within the EU. As Jacobo says, it’s not only about the yogurt.

However, it is worth noting that our goal for Project Aiur is for it to be an autonomous decentralized organization. The legal framework for this is not yet in place in the EU but within a few years it should be, and full legal ownership of the project, not the company, will be transferred (on paper, as it for all intents and purposes will already be) to all token holders.

2. Token ownership

Project Aiur has implemented a 2% ownership cap of tokens. This also applies to Iris.ai the commercial entity, however, in order to facilitate a smooth setup of the project, Iris.ai will hold 50% + 1 token so we can make swifter decisions either the first 18 months of the project, or until the community is self-managed, whichever comes first. After this ‘Phase 1’ we will redistribute (directly or indirectly) 48% + 1 of the tokens out to contributors, token holders and academics so Iris.ai the commercial entity also only holds 2%, or whatever lower cap is set by the community as the project evolves.

3. Community management

As described above, Iris.ai will manage the community for the first 18 months, until all quirks in voting, community management and operations are sorted out. If this is found to happen sooner than 18 months, we will happily redistribute our tokens at that point.

4. Building the Knowledge Validation Engine

Iris.ai the commercial entity will be the first provider of software, building out the roadmap described in the whitepaper. Subject to full community scrutiny, when milestones are completed we will be paid market rate for the work done. Any other software development companies with the appropriate quality level of deliveries can contribute to the building, again subject to community scrutiny. Iris.ai has no desire or capacity to build it all ourselves, we simply guarantee there is someone here ready to build from the get-go.

5. Intellectual Property

All code developed as part of Project Aiur, going into building the Knowledge Validation Engine described in detail in the whitepaper, will belong to the token holders and will be subject to a license with a degree of restriction favoring community members. It can not be duplicated outside of the Aiur community, but it can be utilized by anyone in the community as the core for any third party tools.

6. Business model – for Aiur

For any utilization of the KVE build through Project Aiur, users will have to pay AIUR tokens. Either directly to the community for directly querying the core engine, or via third party applications utilizing the engine. This does not directly give financial rewards to the holders of AIUR tokens, however as the quality of the engine’s intelligence grows and more companies and universities want access to the engine, it is our expectation that the value of the AIUR tokens, isolating other macro factors, should gradually trend upwards over time. Anyone who has contributed to the community, either earning tokens for manual contributions or through participating in the initial crowdsale, should see the value of their contributions validated.

It is worth noting here that the R&D industry, with a $1700Bn yearly budget, spends about $138Bn yearly on so-called ‘digital enablers’. Tools like the KVE holds massive market potential, and Iris.ai’s market experience has shown a number of big clients who are looking for this type of tool, in an API solution they can connect to their internally developed solutions. We have validated both the need and the business model.

7. Business model – for Iris.ai

Iris.ai will be building its future commercial tools on top of the Aiur platform, and you can as such think of us as a third party client of and contributor to the Aiur community. For us at Iris.ai, we will not have exclusivity but we will have a unique first mover advantage on what’s being developed in Project Aiur.

8. So why is Iris.ai doing this?

Project Aiur is a not-for-profit endeavor for the Iris.ai founders. However, it is vital for the success of Iris.ai that Project Aiur exists. The main reason is that we need the Knowledge Validation Engine to exist so that we can continue working towards our goal of building the world’s leading Artificially Intelligent Science Assistant. However, we’re of the firm belief that such an engine cannot be owned by a commercial entity, even by ourselves. So enabling a community to build it is for us a necessary step for our long-term success.

Finally, most important of all, we started Iris.ai as a commercial entity but with a clear impact mission. All of our investors are impact investors, caring about a double bottom line – both the money it makes them and the positive impact the business will have on the world. Initiating Project Aiur is, the way we see it, the most impactful thing we can do for the world of science. It’s not the easiest path we could have taken, but it’s the one that makes us be happy to get up in the morning.

After the initial launch of Iris.ai a lot of interested people have contacted us to find out more about our technology, our future roadmap and the impact we ambition to generate in the world. One of them was our friend from Chalmers University of Technology Christian Berger, Associate professor in Software Engineering at the Computer Science and Engineering Department. Christian invited us to talk about Iris.ai and the future of science with the researchers of his research group.

After a brief presentation, there was an interesting discussion about the daily problems faced by researchers and how much time is wasted doing mapping studies and literature reviews. Talk participants conveyed their fear that even after a couple of months of work there is still no guarantee that the discovered results will cover all the relevant research on any given topic.

A mapping study is important for every research group because it maps all the research within a certain topic and is used for identifying blank spots. These blank spots are areas where new research and innovation is needed. Research mapping studies go over 4-5 years of research and try to direct next steps towards filling the blank spots identified. The problem is that making a good mapping study takes months and there is no guarantee that it will be complete. Even if there are preexisting studies, most research groups still decide to complete their own.

Image credits: CFFC/Ebba Eliasson

During our visit at Chalmers we present the capabilities of Iris.ai and her expected contribution to solving that particular problem. We discussed that Iris.AI, by using her artificial brain, can read much faster and many more sources in no time, presenting to users what she thinks is the relevant landscape around a research topic.

Iris.ai is still a young AI, and she is not there yet in terms of being able to make a full and complete mapping study, but as everyone in the room agreed, she has the potential to make as good a job, or even a better one than a human, saving a lot of time in the process; time that can be used by researchers doing what they are best at: conducting new research. As much as people are interested in what has been done previously, studies show that only 5 to 10% is relevant to specific individuals in their particular area of research. This makes mapping studies not that exciting for the researchers currently tasked with making them.

Another interesting organization full of researchers that want to make our world a safer place was interested in the future of science and how we, as human beings, can make the research process sustainable, more efficient and faster. This organization is called SAFER.

SAFER, Vehicle and Traffic Safety Centre at Chalmers, is a competence center where 34 partners from the Swedish automotive industry, academia and public authorities cooperate to make a center of excellence within the field of vehicle and traffic safety. SAFER is located in Lindholmen Science Park, in Gothenburg, and provides excellent multidisciplinary research and collaboration to eliminate fatalities and serious injuries. They have the goal of supporting the Swedish government achieve zero fatalities caused by traffic accidents.

Image credits: CFFC/Ebba Eliasson

Presenting at their weekly seminar, and as CTO, I led the product demonstration of Iris.AI focusing on her capabilities of grasping the science around one of the audience’s favorite TED Talks – a talk by Chris Urmson: “How a driverless car sees the road” – in just a few seconds.

The audience expressed concerns about how much time is currently spent on making literature reviews and finding connections between research studies in different areas, in order to find a solution to such an interdisciplinary problem as safety on the road. And as some of the people from the audience pointed out, time spent is important because in this particular context safety means lives.

The discussion went into the direction of locating where are the inefficiency spots in the current research process. One of the problems raised was that in the process of researching a topic there is a lot of work that is done several times, inefficiently, until this research finally reaches end users. And one such work package is the literature review.

This literature reviews are conducted by the researchers who do the initial work, by researchers who follow-up, by entrepreneurs and managers at innovation departments who want to check whether implementation is feasible, and, lastly, by corporate development managers when it comes to implementation of the work.

We got positive feedback that the technology we are developing at Iris.AI could help drive more efficient innovation processes. Maybe at the beginning we can do that just by focusing on reducing the workload for people who do not need to go deep into scientific detail, such as entrepreneurs, innovators and corporate development managers, but at later stages we also plan to help interdisciplinary researchers spend less time on work that has already been done by someone else.

We are very glad to meet so many people interested in the future of science, and to be able to engage with them discussing its challenges openly. At Iris.AI we are well aware that we alone will not solve any of the problems currently faced by science. Instead, we want to talk with more and more people about those problems, raising researchers’ awareness and striving towards co-creating solutions with the research community. We also are very happy to see that other people, and researchers in particular, appreciate the potential of our little Iris.AI.

We believe that science is a critically important building block of our society, and making its processes more efficient will help bring forward the solutions to a lot of other pressing global societal problems.